# Fpga Hardware Latency Efficiency

> Evaluates the end-to-end inference and training latency, power consumption, and energy efficiency of a tensorized neural network hardware accelerator on an FPGA platform, comparing against baseline and prior FPGA accelerators. Use when the user has predictions and gold and needs to compute Latency (ms), Energy Efficiency (GOPS/W).

- Skill: `qhjqhj00/fpga-hardware-latency-efficiency` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/fpga-hardware-latency-efficiency`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/fpga-hardware-latency-efficiency/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/fpga-hardware-latency-efficiency

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# fpga-hardware-latency-efficiency

> Comprehensive Design Space Exploration for Tensorized Neural Network Hardware Accelerators — Zhang et al. (2025) (arXiv:2511.17971, 2025)

## What this evaluates

Evaluates the end-to-end inference and training latency, power consumption, and energy efficiency of a tensorized neural network hardware accelerator on an FPGA platform, comparing against baseline and prior FPGA accelerators.

## Datasets

- **CIFAR-10** — total ?; splits: test (-1)
- **Tiny ImageNet** — total ?; splits: test (-1)

## Metrics

- `Latency (ms)` **(primary)** — range: ms
  - End-to-end execution time for inference or training measured via systolic array simulator and FPGA HLS synthesis.
- `Power (W)` — range: W
  - Average power consumption during execution measured on the target FPGA device.
- `Energy Efficiency (GOPS/W)` **(primary)** — range: other
  - Giga Operations Per Second divided by Power consumption in Watts, measuring computational throughput per unit of energy.

## Input / output format

**Input**: Neural network models (ResNet-18, ViT-Ti/4) and datasets (CIFAR-10, Tiny ImageNet) with INT8 quantized parameters, activations, and gradients, mapped to configurable FPGA hardware settings (core partitioning, contraction paths, dataflow mappings).

**Output**: Execution latency in milliseconds, power consumption in Watts, FPGA resource counts (BRAM, FF, LUT, DSP), and energy efficiency in GOPS/W.

## Scoring recipe

```python
def compute_metrics(model, dataset, config):
    latency_ms = measure_execution_time(model, dataset, config)
    power_w = measure_power_consumption(model, dataset, config)
    gops = compute_giga_operations(model, dataset)
    efficiency = gops / power_w
    return latency_ms, power_w, efficiency
```

## Common pitfalls

- Assuming the tensor contraction path with the lowest theoretical MAC count yields the lowest hardware latency; dataflow and core partitioning heavily impact actual latency.
- Comparing results across different prior works without normalizing for dataset, device, and precision; the paper notes direct apples-to-apples comparisons are infeasible except on ResNet-18 on CIFAR-10 INT8.

## Evidence (verbatim from paper)

> Within this widely used evaluation setting, our design achieves a peak energy efficiency of 19.19 GOPS/W in INT8 precision on the VU9P device, delivering 1.27× and 2.13× higher efficiency than (guo2023boost) and (venkataramanaiah2020fpga), respectively, while maintaining moderate BRAM (1482) and DSP (1024) usage.

## Citation

```bibtex
@misc{zhang2025comprehensive,
  title={Comprehensive Design Space Exploration for Tensorized Neural Network Hardware Accelerators},
  author={Zhang et al. (2025)},
  year={2025},
  note={arXiv:2511.17971}
}
```

- arXiv: 2511.17971